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      Structural basis for delta cell paracrine regulation in pancreatic islets

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          Abstract

          Little is known about the role of islet delta cells in regulating blood glucose homeostasis in vivo. Delta cells are important paracrine regulators of beta cell and alpha cell secretory activity, however the structural basis underlying this regulation has yet to be determined. Most delta cells are elongated and have a well-defined cell soma and a filopodia-like structure. Using in vivo optogenetics and high-speed Ca 2+ imaging, we show that these filopodia are dynamic structures that contain a secretory machinery, enabling the delta cell to reach a large number of beta cells within the islet. This provides for efficient regulation of beta cell activity and is modulated by endogenous IGF-1/VEGF-A signaling. In pre-diabetes, delta cells undergo morphological changes that may be a compensation to maintain paracrine regulation of the beta cell. Our data provides an integrated picture of how delta cells can modulate beta cell activity under physiological conditions.

          Abstract

          Pancreatic islets are composed of alpha-, beta-, as well as delta-cells and appropriate regulation of glucose homeostasis relies on auto- and paracrine cellular communication. Here, the authors study the role of delta-cell filopodia in this context by employing optogenetic and calcium imaging approaches.

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          Most cited references35

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          Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

          We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.
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            Diabetes Recovery By Age-Dependent Conversion of Pancreatic δ-Cells Into Insulin Producers

            Total or near-total loss of insulin-producing β-cells is a situation found in diabetes (Type 1, T1D) 1,2 . Restoration of insulin production in T1D is thus a major medical challenge. We previously observed in mice in which β-cells are completely ablated that the pancreas reconstitutes new insulin-producing cells in absence of autoimmunity 3 . The process involves the contribution of islet non-β-cells; specifically, glucagon-producing α-cells begin producing insulin by a process of reprogramming (transdifferentiation) without proliferation 3 . Here we studied the influence of age on β-cell reconstitution from heterologous islet cells after near-total β-cell loss. We found that senescence does not alter α-cell plasticity: α-cells can reprogram to produce insulin from puberty through adulthood, and also in aged individuals, even a long-time after β-cell loss. In contrast, prior to puberty there is no detectable α-cell conversion, although β-cell reconstitution after injury is more efficient, always leading to diabetes recovery; it occurs through a newly discovered mechanism: the spontaneous en masse reprogramming of somatostatin-producing δ-cells. The younglings display “somatostatin-to-insulin” δ-cell conversion, involving de-differentiation, proliferation and re-expression of islet developmental regulators. This juvenile adaptability relies, at least in part, upon combined action of FoxO1 and downstream effectors. Restoration of insulin producing-cells from non-β-cell origins is thus enabled throughout life via δ- or α-cell spontaneous reprogramming. A landscape with multiple intra-islet cell interconversion events is emerging, thus offering new perspectives.
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              A Cre-dependent GCaMP3 reporter mouse for neuronal imaging in vivo.

              Fluorescent calcium indicator proteins, such as GCaMP3, allow imaging of activity in genetically defined neuronal populations. GCaMP3 can be expressed using various gene delivery methods, such as viral infection or electroporation. However, these methods are invasive and provide inhomogeneous and nonstationary expression. Here, we developed a genetic reporter mouse, Ai38, which expresses GCaMP3 in a Cre-dependent manner from the ROSA26 locus, driven by a strong CAG promoter. Crossing Ai38 with appropriate Cre mice produced robust GCaMP3 expression in defined cell populations in the retina, cortex, and cerebellum. In the primary visual cortex, visually evoked GCaMP3 signals showed normal orientation and direction selectivity. GCaMP3 signals were rapid, compared with virally expressed GCaMP3 and synthetic calcium indicators. In the retina, Ai38 allowed imaging spontaneous calcium waves in starburst amacrine cells during development, and light-evoked responses in ganglion cells in adult tissue. Our results show that the Ai38 reporter mouse provides a flexible method for targeted expression of GCaMP3.
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                Author and article information

                Contributors
                per-olof.berggren@ki.se
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                16 August 2019
                16 August 2019
                2019
                : 10
                : 3700
                Affiliations
                [1 ]ISNI 0000 0001 2224 0361, GRID grid.59025.3b, Lee Kong Chian School of Medicine, , Nanyang Technological University, ; Singapore, 636921 Singapore
                [2 ]Karolinska Intitutet, The Rolf Luft Research Center for Diabetes and Endocrinology, Stockholm, 171 77 Sweden
                [3 ]ISNI 0000 0004 0385 0924, GRID grid.428397.3, Molecular Neurophysiology Laboratory, Signature Program in Neuroscience and Behavioral Disorders, , Duke-NUS Medical School, ; Singapore, 169857 Singapore
                [4 ]ISNI 0000 0000 9351 8132, GRID grid.418325.9, Bioinformatics Institute (ASTAR) and Image and Pervasive Access Lab (IPAL), ; Singapore, 138632 Singapore
                [5 ]ISNI 0000 0001 2308 1657, GRID grid.462844.8, Sorbonne University, UPMC University, ; Paris, 75005 France
                [6 ]ISNI 0000 0001 2294 473X, GRID grid.8536.8, Universidade Federal do Rio de Janeiro (UFRJ), ; Rio de Janeiro, 21941-901 Brazil
                [7 ]ISNI 0000 0004 1936 8606, GRID grid.26790.3a, Miller School of Medicine, , University of Miami, ; Miami, 33136 USA
                [8 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, National Center for Microscopy and Imaging Research (NCMIR), , University of California San Diego, ; San Diego, 92093 USA
                [9 ]Present Address: Salk Institute for Biological Studies, Molecular and Cell Biology Laboratory, San Diego, 92037 USA
                Author information
                http://orcid.org/0000-0001-8456-3924
                http://orcid.org/0000-0003-4868-0677
                http://orcid.org/0000-0002-9547-8650
                http://orcid.org/0000-0001-6195-2433
                http://orcid.org/0000-0002-0148-7733
                http://orcid.org/0000-0001-9801-2963
                http://orcid.org/0000-0001-7408-7485
                http://orcid.org/0000-0001-8893-8455
                Article
                11517
                10.1038/s41467-019-11517-x
                6697679
                31420552
                c55a8946-0faf-44d0-b127-ff2cbfaf1f34
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 September 2018
                : 19 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100011738, NTU | Lee Kong Chian School of Medicine, Nanyang Technological University (Lee Kong Chian School of Medicine);
                Funded by: FundRef https://doi.org/10.13039/100007419, Lee Foundation;
                Funded by: FundRef https://doi.org/10.13039/501100004359, Vetenskapsrådet (Swedish Research Council);
                Funded by: FundRef https://doi.org/10.13039/100007436, Familjen Erling-Perssons Stiftelse (Erling-Persson Family Foundation);
                Funded by: FundRef https://doi.org/10.13039/501100011747, Novo Nordisk Foundation Center for Basic Metabolic Research (NovoNordisk Foundation Center for Basic Metabolic Research);
                Funded by: FundRef https://doi.org/10.13039/100009604, Stichting af Jochnick Foundation (Jochnick Foundation);
                Funded by: FundRef https://doi.org/10.13039/501100006354, Berth von Kantzows Stiftelse;
                Funded by: FundRef https://doi.org/10.13039/501100004063, Knut och Alice Wallenbergs Stiftelse (Knut and Alice Wallenberg Foundation);
                Funded by: FundRef https://doi.org/10.13039/501100001961, AXA Research Fund (Le Fonds AXA pour la Recherche);
                Funded by: FundRef https://doi.org/10.13039/501100003593, Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico (National Council for Scientific and Technological Development);
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                physiology,pre-diabetes,cellular imaging
                Uncategorized
                physiology, pre-diabetes, cellular imaging

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